Multivariate Analysis and Machine Learning in Cerebral Palsy Research

Abstract

Cerebral palsy (CP), a common pediatric movement disorder, causes the most severe physical disability in children. Early diagnosis in high-risk infants is critical for early intervention and possible early recovery. In recent years, multivariate analytic and machine learning (ML) approaches have been increasingly used in CP research. This paper aims to identify such multivariate studies and provide an overview of this relatively young field. Studies reviewed in this paper have demonstrated that multivariate analytic methods are useful in identification of risk factors, detection of CP, movement assessment for CP prediction, and outcome assessment, and ML approaches have made it possible to automatically identify movement impairments in high-risk infants. In addition, outcome predictors for surgical treatments have been identified by multivariate outcome studies. To make the multivariate and ML approaches useful in clinical settings, further research with large samples is needed to verify and improve these multivariate methods in risk factor identification, CP detection, movement assessment, and outcome evaluation or prediction. As multivariate analysis, ML and data processing technologies advance in the era of Big Data of this century, it is expected that multivariate analysis and ML will play a bigger role in improving the diagnosis and treatment of CP to reduce mortality and morbidity rates, and enhance patient care for children with CP.

Clasificación de niveles de Medida de la Función Motora Gruesa mediante técnicas de Aprendizaje Automático

“…Los estudios revisados en este documento muestran los métodos analíticos del aprendizaje automático que han sido útiles en la identificación de factores de riesgo, la detección de PC, la evaluación del movimiento para la predicción de la PC y la evaluación de resultados, y los enfoques de ML han permitido identificar automáticamente las alteraciones del movimiento en los pacientes de alto riesgo. Jing Zhang [5] revisa el análisis multivariante y de aprendizaje automático en la investigación de la parálisis cerebral, demostrando que los predictores de resultados para los tratamientos quirúrgicos se han identificado mediante estudios de resultados multivariados. Para que los enfoques multivariante y aprendizaje automático sean útiles en entornos clínicos.…”

AI Approaches towards Prechtl’s Assessment of General Movements: A Systematic Literature Review

“…They discuss the wearable and visual sensors averagely. Zhang [ 40 ] review machine learning methods in cerebral palsy research and evaluates algorithms in movement assessment for CP prediction.…”

Machine learning for monitoring and evaluating physical activity in cerebral palsy

“…3,4 To establish personalized diagnoses and treatments, doctors need to analyse a large amount of data that can be acquired from movement sensors, 1,3 images, assessment tools, and medical and biological records. 2,4 A machine learning model could be the appropriate support in analysing these heterogeneous clinical data collected from patient's electronic health records. 4 However, machine learning-based movement assessment studies are at the early stage of research.…”

“…As data processing technologies advance in the era of big data, machine learning will probably play a bigger role for monitoring and evaluating physical activity, in improving the diagnosis and patient‐specific treatment of CP to reduce morbidity and health care costs 2 …”

“…It uses statistical techniques to ‘learn’ from the observed data and then predict outcomes or categorize observations in unseen data 1 . Examples of machine learning methods include linear discriminate analysis, support vector machine, artificial neural networks, random forest, logistic multiple regression, and cluster analysis 2 . In recent years, machine learning has been increasingly applied to the biomedical field.…”